---
license: mit
language: [en]
pretty_name: "DFADD — Diffusion and Flow-Matching Based Audio Deepfake Dataset"
task_categories: [audio-classification]
size_categories: [1K<n<10K]
configs:
  - config_name: default
    data_files:
      - {split: test, path: "data/test-*.parquet"}
tags:
  - anti-spoofing
  - audio-deepfake-detection
  - speech
  - benchmark
  - arena-ready
  - diffusion
  - flow-matching
arxiv: ["2409.08731"]
---

# DFADD — Diffusion and Flow-Matching Based Audio Deepfake Dataset

Benchmark-ready packaging of the **DFADD** **test (eval) split** (*DFADD: The
Diffusion and Flow-Matching Based Audio Deepfake Dataset*, arXiv 2409.08731), a
VCTK-derived dataset targeting the newest generation of high-quality TTS
spoofing built on **diffusion** and **flow-matching** synthesizers.

## Overview

Binary classification: **bonafide** (genuine VCTK recordings) vs. **spoof**
(text-to-speech generated from VCTK speakers). Label is the top-level source
directory. The spoof side spans **five** modern TTS systems:

| Generator | Family | Spoof clips (test) |
|-----------|--------|--------------------|
| Grad-TTS | diffusion | 600 |
| Matcha-TTS | flow-matching | 600 |
| NaturalSpeech 2 | latent diffusion | 600 |
| PFlow-TTS | flow-matching | 600 |
| StyleTTS 2 | diffusion (style) | 600 |

The bonafide side is the genuine VCTK audio held out for the test split (755 clips).

## License & redistribution

Released under the **MIT License** (per the upstream DFADD release). Redistribution
is permitted with attribution; see `LICENSE.txt`. The underlying speech derives from
the **VCTK Corpus** (CC BY 4.0) — attribute both DFADD and VCTK. Audio is the
original 16 kHz mono FLAC/WAV, embedded bit-exactly (no re-encode — a full decode
probe of all test clips passed cleanly).

## Schema

| Column | Type | Description |
|--------|------|-------------|
| `path` | `string` | source-relative path, e.g. `DATASET_GradTTS/test/p227_001_GradTTS.flac`, unique |
| `audio` | `Audio(16000)` | 16 kHz mono |
| `label` | `ClassLabel` | `"bonafide"` (0) / `"spoof"` (1) |
| `notes` | `string` | JSON: `utterance_id`, `generator`, `speaker`, `attack` |

`notes` example:
```json
{"utterance_id": "gradtts__p227_001_GradTTS", "generator": "gradtts", "speaker": "p227", "attack": "gradtts"}
```

`utterance_id` is `<generator>__<filename-stem>` — the bare stem repeats across
generators (the same VCTK texts are synthesized), so the generator prefix is what
makes ids unique. Bonafide ids use the `vctk__` prefix.

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("SpeechAntiSpoofingBenchmarks/DFADD", split="test")
print(ds[0])
```

## Stats

| Stat | Value |
|------|-------|
| Total trials | 3,755 |
| Bonafide (VCTK) | 755 |
| Spoof (TTS) | 3,000 |
| Generators | Grad-TTS, Matcha-TTS, NaturalSpeech 2, PFlow-TTS, StyleTTS 2 (600 each) |
| Sample rate | 16 kHz mono |

## Source provenance

- Paper: *DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset*,
  arXiv 2409.08731 (https://arxiv.org/abs/2409.08731).
- Underlying speech: the **VCTK Corpus** (CC BY 4.0).

## Evaluation

For evaluation instructions and submission format, see [`submissions/README.md`](submissions/README.md).

## Citation

```bibtex
@article{du2024dfadd,
  title   = {{DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset}},
  author  = {Du, Jiawei and others},
  journal = {arXiv preprint arXiv:2409.08731},
  year    = {2024},
}
```

## Maintainer

Contact: k.n.borodin@mtuci.ru